Gantry alignment of a medical scanner

ABSTRACT

A framework for gantry alignment of a multimodality medical scanner. First image data of a non-radioactive structure is acquired by using intrinsic radiation emitted by scintillator crystals of detectors in a first gantry of the multimodality medical scanner. Second image data of the non-radioactive structure is acquired using a second gantry for another modality of the multimodality medical scanner. Image reconstruction may be performed based on the first and second image data of the non-radioactive structure to generate first and second reconstructed image volumes. A gantry alignment transformation that aligns the first and second reconstructed image volumes may then be determined.

TECHNICAL FIELD

The present disclosure generally relates to image data processing, andmore particularly to a framework for gantry alignment of a medicalscanner.

BACKGROUND

Multimodality imaging plays an important role in accurately identifyingdiseased and normal tissues. Multimodality imaging provides combinedbenefits by fusing images acquired by different modalities. Thecomplementarity between anatomic (e.g., computed tomography (CT) andmagnetic resonance (MR) imaging) and molecular (e.g., positron-emissiontomography (PET)) imaging modalities, for instance, has led to thewidespread use of PET/CT and PET/MR imaging.

Multimodality scanners require a procedure to measure the spatialdisplacement between images produced by the different modalities (e.g.,PET to CT displacement, or PET to MR displacement). This procedure iscommonly referred to as “gantry alignment”. The standard gantryalignment procedure uses radioactive sources or hot phantoms thatfacilitate the acquisition of PET emission data to form the PET image.Radioactive sources (e.g. points sources, line sources) are typicallypositioned in a specific arrangement and imaged using, for example, bothPET and CT for a PET/CT scanner, or both PET and MR for a PET/MRscanner.

For a PET/CT scanner, the radioactive source material typically producessufficient X-ray attenuation to produce a CT image. For a PET/MRscanner, the radioactive source material is typically not visible in MRimaging sequences, so the radioactive sources are traditionallysurrounded by an MR-visible material, such as oil, which produces an MRimage. The MR-invisible radioactive source material results in a void inthe MR images, which is used to identify the location of the sources.

However, such gantry alignment procedure that relies on an externalradioactive positron source is typically time consuming, since thesource must be maintained and carefully placed in the center of thegantry of the scanner. Additionally, a human needs to handle the sourcerepeatedly, which leads to health and safety risks due to theradioactivity of the source.

SUMMARY

Described herein is a framework for gantry alignment of a multimodalitymedical scanner. First image data of a non-radioactive structure isacquired by using intrinsic radiation emitted by scintillator crystalsof detectors in a first gantry of the multimodality medical scanner.Second image data of the non-radioactive structure is acquired using asecond gantry for another modality of the multimodality medical scanner.Image reconstruction may be performed based on the first and secondimage data of the non-radioactive structure to generate first and secondreconstructed image volumes. A gantry alignment transformation thataligns the first and second reconstructed image volumes may then bedetermined.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the present disclosure and many of theattendant aspects thereof will be readily obtained as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in connection with the accompanying drawings.

FIG. 1 shows a block diagram illustrating an exemplary system;

FIG. 2 shows an exemplary method of gantry alignment;

FIG. 3 shows an exemplary non-radioactive structure;

FIG. 4 shows another exemplary non-radioactive structure;

FIG. 5 shows exemplary local projection images;

FIG. 6 a shows an exemplary gantry alignment transformation; and

FIG. 6 b shows the residual error.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthsuch as examples of specific components, devices, methods, etc., inorder to provide a thorough understanding of implementations of thepresent framework. It will be apparent, however, to one skilled in theart that these specific details need not be employed to practiceimplementations of the present framework. In other instances, well-knownmaterials or methods have not been described in detail in order to avoidunnecessarily obscuring implementations of the present framework. Whilethe present framework is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that there is no intent to limit theinvention to the particular forms disclosed, but on the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the invention. Furthermore, forease of understanding, certain method steps are delineated as separatesteps; however, these separately delineated steps should not beconstrued as necessarily order dependent in their performance.

Unless stated otherwise as apparent from the following discussion, itwill be appreciated that terms such as “segmenting,” “generating,”“registering,” “determining,” “aligning,” “positioning,” “processing,”“computing,” “selecting,” “estimating,” “detecting,” “tracking” or thelike may refer to the actions and processes of a computer system, orsimilar electronic computing device, that manipulates and transformsdata represented as physical (e.g., electronic) quantities within thecomputer system's registers and memories into other data similarlyrepresented as physical quantities within the computer system memoriesor registers or other such information storage, transmission or displaydevices. Embodiments of the methods described herein may be implementedusing computer software. If written in a programming language conformingto a recognized standard, sequences of instructions designed toimplement the methods can be compiled for execution on a variety ofhardware platforms and for interface to a variety of operating systems.In addition, implementations of the present framework are not describedwith reference to any particular programming language. It will beappreciated that a variety of programming languages may be used.

A framework for gantry alignment is presented herein. In accordance withone aspect, a non-radioactive structure (or phantom) is used for gantryalignment of a multimodality scanner (e.g., PET/CT and PET/MR).Intrinsic radiation from scintillator crystals in the multimodalityscanner is used to create transmission images by measuring transmissionof photons through a non-radioactive structure. In some implementations,neural networks may be used to determine gantry offsets from thetransmission images and/or denoise short-duration scintillator crystalstransmission images.

Advantageously, no radioactive sources are required, thereby minimizingongoing cost to customers to purchase and replace sources. The presentframework is more efficient and safer as it does not require aradioactive source to be maintained, and it avoids the health and safetyconcerns associated with radioactivity. It also advantageously minimizescustomer licensing overheads required to store radioactive sources.These and other exemplary advantages and features will be described inmore details in the following description.

FIG. 1 is a block diagram illustrating an exemplary system 100. Thesystem 100 includes a computer system 101 for implementing the frameworkas described herein. In some implementations, computer system 101operates as a standalone device. In other implementations, computersystem 101 may be connected (e.g., using a network) to other machines,such as multimodality medical scanner 130 and workstation 134. In anetworked deployment, computer system 101 may operate in the capacity ofa server (e.g., in a server-client user network environment, a clientuser machine in server-client user network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

In one implementation, computer system 101 includes a processor deviceor central processing unit (CPU) 104 coupled to one or morenon-transitory computer-readable media 106 (e.g., computer storage ormemory device), display device 108 (e.g., monitor) and various inputdevices 109 (e.g., mouse, touchpad or keyboard) via an input-outputinterface 121. Computer system 101 may further include support circuitssuch as a cache, a power supply, clock circuits and a communicationsbus. Various other peripheral devices, such as additional data storagedevices and printing devices, may also be connected to the computersystem 101.

The present technology may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof, either as part of the microinstruction code or as part of anapplication program or software product, or a combination thereof, whichis executed via the operating system. In some implementations, thetechniques described herein are implemented as computer-readable programcode tangibly embodied in one or more non-transitory computer-readablemedia 106. In particular, the present techniques may be implemented by aprocessing module 117. Non-transitory computer-readable media 106 mayinclude random access memory (RAM), read-only memory (ROM), magneticfloppy disk, flash memory, and other types of memories, or a combinationthereof. The computer-readable program code is executed by CPU 104 toprocess data provided by, for example, database 119 and/or multimodalitymedical scanner 130. As such, the computer system 101 is ageneral-purpose computer system that becomes a specific-purpose computersystem when executing the computer-readable program code. Thecomputer-readable program code is not intended to be limited to anyparticular programming language and implementation thereof. It will beappreciated that a variety of programming languages and coding thereofmay be used to implement the teachings of the disclosure containedherein. The same or different computer-readable media 106 may be usedfor storing a database 119, including, but not limited to, imagedatasets, a knowledge base, individual subject data, medical records,diagnostic reports (or documents) for subjects, or a combinationthereof.

Multimodality medical scanner 130 acquires image data 132 associatedwith at least one subject. Such image data 132 may be processed andstored in database 119. Multimodality medical scanner 130 may be aradiology scanner (e.g., nuclear medicine scanner) and/or appropriateperipherals (e.g., keyboard and display device) for acquiring,collecting and/or storing such image data 132.

Multimodality medical scanner 130 may be a hybrid modality designed foracquiring image data using at least one modality that uses scintillatorcrystals for detection (e.g., PET). For example, multimodality medicalscanner 130 may include a first gantry for PET imaging and a secondgantry for CT or MR imaging. The PET imaging gantry may include aplurality of detectors comprising scintillator crystals. Thescintillator crystals may be lutetium-based scintillator crystals, suchas scintillator crystals including lutetium orthosilicate (LSO) orlutetium yttrium orthosilicate (LYSO), that include the radioactiveisotope Lu-176. A scintillator (or scintillation crystal) in a PETscanner typically detects one of the gamma photons originating from theannihilation event, and another scintillator crystal detects the othergamma photon. The scintillator crystals are typically part of detectorsthat are arranged in a circular or cylindrical configuration around theregion where the patient lies. When struck with a gamma photon, eachscintillator crystal emits a flash of visible light that is converted toelectrons by a photomultiplier tube (PMT) or Silicon Photomultiplier(SiPM) of the PET scanner for subsequent electrical processing.Intrinsic radiation emitted from the scintillator crystals is used toacquire a transmission image of a non-radioactive structure. See, forexample, Rothfuss H, Panin V, Moor A, Young J, Hong I, Michel C, HamillJ, Casey M, LSO background radiation as a transmission source using timeof flight, Phys Med Biol. 2014 Sep. 21; 59(18):5483-500, and U.S. PatentApplication 20140217294, which are both incorporated herein by referencein their entirety.

The workstation 134 may include a computer and appropriate peripherals,such as a keyboard and display device, and can be operated inconjunction with the entire system 100. For example, the workstation 134may communicate with multimodality medical scanner 130 so that themedical image data 132 from multimodality medical scanner 130 can bepresented or displayed at the workstation 134. The workstation 134 maycommunicate directly with the computer system 101 to display processeddata and/or output results 144. The workstation 134 may include agraphical user interface to receive user input via an input device(e.g., keyboard, mouse, touch screen, voice or video recognitioninterface, etc.) to manipulate visualization and/or processing of thedata.

It is to be further understood that, because some of the constituentsystem components and method steps depicted in the accompanying figurescan be implemented in software, the actual connections between thesystems components (or the process steps) may differ depending upon themanner in which the present framework is programmed. Given the teachingsprovided herein, one of ordinary skill in the related art will be ableto contemplate these and similar implementations or configurations ofthe present framework.

FIG. 2 shows an exemplary method 200 of gantry alignment. It should beunderstood that the steps of the method 200 may be performed in theorder shown or a different order. Additional, different, or fewer stepsmay also be provided. Further, the method 200 may be implemented withthe system 100 of FIG. 1 , a different system, or a combination thereof.

At 202, a non-radioactive structure (or phantom) is positioned in afield-of-view of a multimodality medical scanner 130. FIG. 3 shows anexemplary non-radioactive structure 302. The three-dimensional (3D)non-radioactive structure 302 may be positioned on, for example, apatient table 304 for image acquisition. Alternatively, thenon-radioactive structure 302 may be held by a bracket or other supportso that it extends into the imaging field-of-view without being locatedon or above the patient table. In some implementations, thenon-radioactive structure 302 includes objects with material and layoutthat intentionally minimize the overall attenuation of photons emittedfrom the scintillator crystals. The highly attenuating objects arepreferably placed only in locations that contribute constructively tothe process which determines the spatial orientation of the structure302 in the reconstructed transmission image.

For MR imaging, both the composite material and the geometric layout ofthe phantom structure are selected to consider the uniformity of the MRmagnetic field. Spatial distortion in the MR image can result from somematerials with high magnetic susceptibilities, and from someasymmetrical geometries. The structure includes objects thatsubstantially attenuate photons from the scintillator crystals andproduce an MR image (i.e., MR visible). The objects are distributed orsupported by material with low-attenuating properties and that issubstantially MR invisible.

The exemplary 3D structure 302 minimizes MR image artefacts, minimizesglobal photon attenuation, and produces sufficient photon attenuation indesired locations and MR signal to provide both transmission and MRimages that can be used to determine spatial offsets between thegantries. The 3D structure 302 includes 3D objects 308 that areconstructed from, or filled with, a material that is visible with MR,such as, but not limited to, liquid nickel sulfate (NiSO4). 3D objects308 may include, for example, spheres or other non-spherical (e.g.,cubic) objects. The 3D objects 308 are placed in low density material(e.g., polymer foam) substrate 310. The low density material substrate310 is substantially invisible in standard MR sequences and minimallyattenuates the photons from the scintillator crystals.

For CT imaging, the structure 302 includes objects 308 that attenuatephotons from the scintillator crystals and X-rays, and are visible withCT. The objects 308 are distributed or supported in material withlow-attenuating properties and result in substantially low HounsfieldUnited (HU) in the reconstructed CT image. One non-limiting example ofsuch material is liquid nickel sulfate (NiSO4). Other materials may alsobe used. The 3D objects 308 are placed in low density material (e.g.,polymer foam) substrate 310, which results in minimal attenuation ofboth scintillator photons used to create the transmission image as wellas X-ray photons used to create the CT image.

FIG. 4 shows another exemplary non-radioactive structure 402. In thisexample, NiSO4 filled spheres 404 are connected with rods 406 to form apyramidal structure. Other types of structures, such as tetrahedral oroctahedral structures, are also possible. Rods 406 may be made ofpolytetrafluoroethylene (PTFE) or any other suitable material. Spheres404 and rods 406 are visible in transmission and CT images. In MRimages, only the spheres 404 are visible, while the PTFE rods 406 do notappear in the images or create any spatial distortion.

Returning to FIG. 2 , at 204, first image data of the non-radioactivestructure is acquired by using intrinsic radiation emitted byscintillator crystals in the multimodality medical scanner 130. Thescintillator crystals may be found in PET detectors of a first gantry inthe multimodality medical scanner 130. In some implementations, thescintillator crystals are Lutetium-based scintillator crystals, whichare known to have intrinsic radiation that originates from the isotopeLu-176, which is 2.6% abundant in natural occurring lutetium. Lu-176decay through beta decay with cascading gammas having energies of 307,202 and 88 keV. The first image data (or transmission data) acquiredfrom the Lu-176 decay may be separated into sinograms depending on thegamma photons' energies. Sinograms may be reconstructed using, forexample, the Maximum Likelihood Transmission (ML-TR) algorithm, which isused to reconstruct the transmission image.

At 206, second image data of the non-radioactive structure is acquiredby using the second gantry for another modality in the multimodalitymedical scanner 130. The second modality may be, for example, CT or MR.The non-radioactive structure is preferably not moved between the firstand second image data acquisitions. In the case of PET/MR, an MRsequence is either simultaneously, partially simultaneously, orsequentially executed.

At 208, processing module 117 performs reconstruction based on the firstand second image data to generate first and second reconstructed imagevolumes respectively. The first reconstructed image volume may be avolumetric transmission (Tx) image (e.g., LSO-Tx image) that isreconstructed from the first image data using reconstruction methodssuch as Maximum Likelihood Transmission (ML-TR) image reconstructiontechnique. The ML-TR image reconstruction technique is an iterativealgorithm with quadratic regularization that models the transmissiondata statistics. See, for example, Rothfuss H, Panin V, Moor A, Young J,Hong I, Michel C, Hamill J, Casey M, LSO background radiation as atransmission source using time of flight, Phys Med Biol. 2014 Sep. 21;59(18):5483-500, which is herein incorporated by reference in itsentirety. The first reconstructed image volume (e.g., LSO-Tx image)replaces the acquired and reconstructed PET emission image ofradioactive sources typically used in standard gantry alignmentprocedures.

The second reconstructed image volume may be, for example, a volumetricCT or MR image reconstructed from the second image data using areconstruction technique such as an iterative reconstruction algorithm,filtered backprojection, statistical modeling or a combination thereof.Other techniques are also useful.

At 210, processing module 117 determines gantry alignment transformationto align first and second reconstructed image volumes. The gantryalignment transformation describes the mechanical displacement (orspatial offset) between the first and second gantries in multimodalitymedical scanner 130. The gantry alignment transformation identifies oneor more rotations and/or translations required to align the first andsecond reconstructed image volumes. For example, the gantry alignmenttransformation includes 3 translation values corresponding to the 3primary orthogonal axes in the image domain (e.g. anterior-posterior,inferior-superior and left-right) and 3 rotational values about theseorthogonal axes. Applying the gantry alignment transformation to thefirst reconstructed image volume (e.g., reconstructed PET images)produces images that are spatially aligned to the second reconstructedimage volume (e.g., CT images in the case of PET/CT systems or MR imagesin the case of PET/MR systems).

In some implementations, analytical methods are used to determine thegantry alignment transformation. One example of an analytical methodutilizes affine registration to align the first and second two imagevolumes using a mutual information-based cost function. See, forexample, P. Viola and W. M. Wells, “Alignment by maximization of mutualinformation,” Proceedings of IEEE International Conference on ComputerVision, Cambridge, MA, USA, 1995, pp. 16-23, which is hereinincorporated by reference in its entirety. Other cost functions are alsopossible. Another exemplary analytical method explicitly identifies eachsphere in the non-radioactive structure in the first and secondreconstructed image volumes, and determines the transformation thatminimizes the distance between sphere-centers. Sphere centers may beidentified by calculating the center of mass of each sphere, or by usingprior knowledge of the sphere geometry and locating the spheres using aHough Transform.

FIG. 5 shows exemplary local projection images 502 derived from areconstructed CT image volume (i.e., second reconstructed image volume)and exemplary local projection images 504 derived from a reconstructedLSO-TX image volume (i.e., first reconstructed image volume). The localprojection images may be x-z projection images and x-y projectionimages. Hough Transform may be used to locate the spheres 506 and 508 inthe respective projection images 502 and 504. A gantry alignmenttransformation that minimizes the distance between the centers ofspheres 506 and the centers of spheres 508 may then be determined.

In other implementations, analytical methods are used with deep-learningtechniques for denoising prior to determining the gantry alignmenttransformation. Acquisition time for the first image data (i.e.,transmission image) of the non-radioactive structure is reduced bydenoising. To achieve this, a deep-learning neural network may betrained to approximate a full (or long) duration denoised transmissionimage (i.e., first image data) from a reduced duration image. Multiplepairs of full-duration and reduced-duration transmission images are usedto train the neural network, wherein each reduced-duration transmissionimage is a subset of the events recorded in the correspondingfull-duration transmission image. Alternatively, CT images acquired by ascanner aligned using existing gantry alignment methods may be utilized,only during training, to correlate the LSO TX images to a matching CTdataset and thereby serve as a target for the neural network.

In yet other implementations, deep learning techniques are used withoutanalytical methods to directly determine the gantry alignmenttransformation. In this approach, transmission images (i.e., first imagedata) and CT or MR images (i.e., second image data) are acquired andreconstructed, and a deep learning convolutional neural network is usedto directly identify the translations and/or rotations that describe thespatial offset between the two gantries of multimodality medical scanner130. The neural network may be trained with multiple instances ofmatching first and second reconstructed image volumes (e.g.,transmission and CT or MR reconstructed image pairs) on systems withknown gantry offsets. The known gantry offset may be obtained by usingradioactive marker (or hot phantom) as described in the standardNational Electrical Manufacturers Association (NEMA) gantry offsetprocedure.

FIG. 6 a shows an exemplary gantry alignment transformation 602 for aCT-PET medical scanner, as determined by the present framework. Asshown, the gantry alignment transformation 602 specifies a rotation(i.e., roll, yaw, pitch) and translation about X, Y and Z axes. FIG. 6 bshows the residual error 604 in the resultant gantry alignmenttransformation. The residual error is determined for each of the 5spheres in the non-radioactive structure. It can be observed that themean residual error is 0.00 for all three axes. The mean absolute (abs)error is 0.95 mm, 0.37 mm and 0.30 mm for the X, Y and Z axesrespectively.

While the present framework has been described in detail with referenceto exemplary embodiments, those skilled in the art will appreciate thatvarious modifications and substitutions can be made thereto withoutdeparting from the spirit and scope of the invention as set forth in theappended claims. For example, elements and/or features of differentexemplary embodiments may be combined with each other and/or substitutedfor each other within the scope of this disclosure and appended claims.

What is claimed is:
 1. A gantry alignment system, comprising: anon-radioactive structure positioned in a field-of-view of amultimodality medical scanner; a non-transitory memory device forstoring computer readable program code; and a processor device incommunication with the memory device, the processor device beingoperative with the computer readable program code to perform stepsincluding performing image reconstruction based on first and secondimage data of the non-radioactive structure to generate first and secondreconstructed image volumes, wherein the first image data is acquired byusing intrinsic radiation emitted by scintillator crystals ofpositron-emission tomography (PET) detectors in the multimodalitymedical scanner, wherein the second image data is acquired usingmagnetic resonance (MR) or computed tomography (CT) of the multimodalitymedical scanner, and determining a gantry alignment transformation thataligns the first and second reconstructed image volumes.
 2. The gantryalignment system of claim 1 wherein the scintillator crystals compriselutetium-based scintillator crystals.
 3. The gantry alignment system ofclaim 2 wherein the lutetium-based scintillator crystals compriselutetium orthosilicate (LSO) or lutetium yttrium orthosilicate (LYSO).4. The gantry alignment system of claim 1 wherein the non-radioactivestructure comprises objects constructed from or filled with a materialthat substantially attenuates photons from the scintillator crystals andproduces an MR or CT signal.
 5. The gantry alignment system of claim 4wherein the material comprises nickel sulfate.
 6. The gantry alignmentsystem of claim 4 wherein the objects are placed in a substrate that issubstantially invisible in MR or CT and minimally attenuates the photonsfrom the scintillator crystals.
 7. The gantry alignment system of claim6 wherein the substrate comprises a polymer substrate.
 8. The gantryalignment system of claim 4 wherein the objects comprise spheresconnected with at least one rod.
 9. The gantry alignment system of claim8 wherein the at least one rod comprises polytetrafluoroethylene.
 10. Agantry alignment method, comprising: acquiring first image data of anon-radioactive structure by using intrinsic radiation emitted byscintillator crystals of detectors in a first gantry of a multimodalitymedical scanner; acquiring second image data of the non-radioactivestructure using a second gantry for another modality of themultimodality medical scanner; performing image reconstruction based onthe first and second image data of the non-radioactive structure togenerate first and second reconstructed image volumes; and determining agantry alignment transformation that aligns the first and secondreconstructed image volumes.
 11. The gantry alignment method of claim 10wherein acquiring the first image data of the non-radioactive structurecomprises using the intrinsic radiation emitted by Lutetium-basedscintillator crystals in positron-emission tomography (PET) detectors togenerate the first image data.
 12. The gantry alignment method of claim10 wherein acquiring the second image data comprises acquiring acomputed tomography (CT) or magnetic resonance (MR) image.
 13. Thegantry alignment method of claim 10 wherein performing the imagereconstruction based on the first and second image data of thenon-radioactive structure comprises performing a Maximum LikelihoodTransmission image reconstruction technique to generate the firstreconstructed image volume.
 14. The gantry alignment method of claim 10wherein determining the gantry alignment transformation comprisesdetermining an affine registration to align the first and secondreconstructed image volumes.
 15. The gantry alignment method of claim 10wherein determining the gantry alignment transformation comprisesidentifying spheres in the non-radioactive structure in the first andsecond reconstructed image volumes, and determining the gantry alignmenttransformation that minimizes distance between centers of the spheres.16. The gantry alignment method of claim 15 wherein identifying thespheres comprises locating the spheres using a Hough Transform.
 17. Thegantry alignment method of claim 10 wherein acquiring the first imagedata of the non-radioactive structure comprises acquiring a reducedduration first image data, and approximating a full duration first imagedata by applying a trained deep-learning neural network to the reducedduration first image data.
 18. The gantry alignment method of claim 10wherein determining the gantry alignment transformation comprises usinga deep learning convolutional neural network to identify the gantryalignment transformation.
 19. The gantry alignment method of claim 18further comprises training the deep learning convolutional neuralnetwork with multiple instances of matching the first and secondreconstructed image volumes on systems with known gantry offsets. 20.One or more non-transitory computer-readable media embodyinginstructions executable by a machine to perform operations for gantryalignment comprising: acquiring first image data of a non-radioactivestructure by using intrinsic radiation emitted by scintillator crystalsof detectors in a first gantry of a multimodality medical scanner;acquiring second image data of the non-radioactive structure using asecond gantry for another modality of the multimodality medical scanner;performing image reconstruction based on the first and second image dataof the non-radioactive structure to generate first and secondreconstructed image volumes; and determining a gantry alignmenttransformation that aligns the first and second reconstructed imagevolumes.